2017 Projects

Encryption via a secret key has been the traditional mode of ensuring security up through the current day. However, due to the increase in high-performance computing the reliability of traditional key encryption has diminished. Physical layer (PHY) security provides a solution that establishes a shared secret key over an unsecured communication channel via the wireless medium. Considering these two phenomena, the main idea of physical layer security lies in the shared wireless channel between two communicating nodes or a transmitter and receiver (TR) pair. Both TR observe a specific link signature (LS) upon various factors. With this seminal foundation along with previous research, we set up an experiment from the perspective of an attacker attempting to exploit the TR's LS with capabilities realistic to a real world attack. Our motivation is to exhaust the vulnerabilities of PHY security to aid in making it more robust in the future. With the use of machine learning (ML) algorithms the experimental results confirm that LS of TR is exploitable with the use of ML.

As Android holds a greater than 80% market share as of Q4 of 2016, it has become a prime target for cyber attacks in recent years. Although there are many ways for malware to enter your device, an increasingly popular method is through malicious applications. There are many malware detection and mitigation techniques implemented by antivirus applications on the Google Play Store, however their limited administrative capabilities under the Android OS leave them wanting for power. Our intent is to utilize hardware analysis to develop a more robust and comprehensive malware detection application than those that utilize static and/or dynamic techniques alone.

With the proliferation of the cloud computing paradigm, there is an increasing need to build sophisticated networks in a timely manner and adapt to dynamic business requirements. To meet these requirements, the artifacts in the network, such as the network topology, IP addresses, security policies, and routing rules, need to be scalable, elastic, and easy to be customized. In this project, we plan to use the software defined network (SDN) as the key technology to construct a virtual laboratory environment, which can handle the special requirements of a complex network. We will use CloudLab to setup different domains and build a scalable network. In addition, we will use the programming languages, such as Python, Java, C, and Bash, to implement the key functionalities of the system, such as setting up the security policies and changing the routing rules.

Code smells are poor design choices that, while not necessarily bugs, can negatively impact the quality of an application. Some affect the maintainability of the code, while others can break programs. Our task is to learn about the code smells that are applicable to JavaScript applications. From there we will use Type Analyzer for JavaScript to build a flow graph modeling JavaScript programs and study the patterns that these code smells generate. From these patterns we will be able to modify the TAJS source code to detect the code smells.

As the Internet of Things (IoT) becomes more prevalent in society, security threats continue to arise. Many IoT devices, such as smart cameras and DVRs, are sold with little to no security allowing hackers to use many different types of attacks to compromise either their functionality, or the privacy of their owners. The first step to preventing these attacks is being able to identify patterns which indicate the types of attacks being performed: denial of service (DoS), probe, remote to local (r2l), and unauthorized access to root (u2r) are all general types of attacks used to compromise or interfere with IoT devices. As with type, being able to identify the intention behind an attack will also enable more efficient prevention of attacks. In most instances, an attacker will remain hidden, so attacks must be identified based on the traces left behind as the attack occurs. Thus, the goal of this research is to apply extended Hidden Markov Models (HMMs) to two dimensions to detect the type as well as intention behind attacks on IoT devices.
Internet of Things (IoT) attacks have rapidly risen in frequency in recent years as IoT devices become more commonplace in industry, businesses, and homes. Since these devices have very basic functionality and are not designed with security in mind, they are easy targets for attacks that can steal data or gain access to the network the devices are connected to. Here we propose a tiered system of Hidden Markov Models (HMMs) for identifying these attacks and classifying them by type of attack. This system has a tree-based structure, with the main HMM being applied to the raw network data to identify attacks. This main HMM branches off into separate HMMs for each type of attack to classify the attacks according to how important the consequences of the attack are and how likely each attack is to happen.